FEMS Microbiology Ecology, 93, 2017, fix128 doi: 10.1093/femsec/fix128 Advance Access Publication Date: 3 October 2017 Research Article
RESEARCH ARTICLE
Comparison of Thaumarchaeotal populations from four deep sea basins Stephen M. Techtman1 , Nagissa Mahmoudi2 , Kendall T. Whitt2 , Maria Fernanda Campa3,4 , Julian L. Fortney2,3 , Dominique C. Joyner2,3 and Terry C. Hazen2,3,4,5,6,7,8,∗ 1
Department of Biological Sciences, Michigan Technological University, Houghton MI 49931-1295, USA, Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA , 3 Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN 37996, USA , 4 Bredesen Center, University of Tennessee, Knoxville, TN 37996, USA , 5 Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, TN 37996, USA , 6 Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA, 7 Department of Microbiology, University of Tennessee, Knoxville, TN 37916, USA and 8 Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN 37996, USA 2
∗
Corresponding author: Department of Civil and Environmental Engineering, University of Tennessee, 507 SERF, Knoxville, TN 37996 USA. Tel: 865-974-7709; E-mail:
[email protected] One sentence summary: Archaea are dominant in the water column in most deep marine basins; however, geographically distant, yet geochemically similar basins may house distinct ammonium-oxidizing Thaumarchaeaotal populations. Editor: Patricia Sobecky
ABSTRACT The nitrogen cycle in the marine environment is strongly affected by ammonia-oxidizing Thaumarchaeota. In some marine settings, Thaumarchaeotes can comprise a large percentage of the prokaryotic population. To better understand the biogeographic patterns of Thaumarchaeotes, we sought to investigate differences in their abundance and phylogenetic diversity between geographically distinct basins. Samples were collected from four marine basins (The Caspian Sea, the Great Australian Bight, and the Central and Eastern Mediterranean). The concentration of bacterial and archaeal 16S rRNA genes and archaeal amoA genes were assessed using qPCR. Minimum entropy decomposition was used to elucidate the fine-scale diversity of Thaumarchaeotes. We demonstrated that there were significant differences in the abundance and diversity of Thaumarchaeotes between these four basins. The diversity of Thaumarchaeotal oligotypes differed between basins with many oligotypes only present in one of the four basins, which suggests that their distribution showed biogeographic patterning. There were also significant differences in Thaumarchaeotal community structure between these basins. This would suggest that geographically distant, yet geochemically similar basins may house distinct Thaumarchaeaotal populations. These findings suggest that Thaumarchaeota are very diverse and that biogeography in part contributes in determining the diversity and distribution of Thaumarchaeotes. Keywords: Thaumarchaeota; qPCR; minimum entropy decomposition; biogeography
Received: 7 December 2016; Accepted: 29 September 2017 C FEMS 2017. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
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INTRODUCTION Thaumarchaeota comprise a large proportion of the active microbial community in the oceans and play a significant role in marine nitrogen and carbon cycling (Karner, DeLong and Karl 2001; Francis et al. 2005; Konneke et al. 2005; Ingalls et al. 2006; Brochier-Armanet et al. 2008; Hollibaugh et al. 2011; Pester, Schleper and Wagner 2011; Yakimov et al. 2011; Baker, Lesniewski and Dick 2012). Thaumarchaeotes are primarily involved in nitrification, particularly in the process of ammonia oxidation (Wuchter et al. 2006; Prosser and Nicol 2008). The phylogenetic diversity of marine Thaumarchaeotes has been investigated in a number of settings (Bergauer et al. 2013; Hu et al. 2013; Tolar, King and Hollibaugh 2013; Swan et al. 2014). The ubiquitous nature of the Thaumarchaeotal phylum suggests that they are able to thrive in diverse environments and compete for available resources under vastly different conditions. Despite their cosmopolitan nature, a few studies have reported biogeographic patterns in the distribution of Thaumarchaeotes, which are partitioned by location and habitat (Biller et al. 2012; Peng, Jayakumar and Ward 2013; Sintes et al. 2013). These differences appear to occur most strongly between habitats (i.e. marine water column versus soils). These habitats show drastically different environmental characteristics and correspondingly great differences in the phylogenetic diversity of Thaumarchaeotes present. More recent studies have shown that biogeographic patterns in Thaumarchaetoal communities correspond in part to the availability of ammonia in these different locations (Sintes et al. 2013) Thaumarchaeotes have been extremely difficult to isolate, limiting our understanding of this phylum. Two recent studies have sought to investigate the genomic diversity of deep sea Thaumarchaeotes using single-cell genomics (Luo et al. 2014; Swan et al. 2014). These studies demonstrated that there are genomic similarities between Thaumarchaeotes collected at similar depths, which was in line with previously reported results indicating that there are depth-associated ecotypes of Thaumarchaeotes (Francis et al. 2005). The Single Amplified Genomes obtained in these studies possessed many genes not encoded in the model cultured Thaumarchaeote—Nitrosopumilus maritimus (Luo et al. 2014; Swan et al. 2014). This finding suggests that there is greater genetic diversity within the Thaumarchaeota than previously identified through studies on the few cultured representatives such as N. maritimus. Thaumarcheaotal abundance has been shown to vary relative to environmental conditions including temperature, dissolved oxygen and salinity among others (Hatzenpichler 2012). Therefore, different environmental conditions experienced in different oceanic basins may impact the abundance of Thaumarchaeotes. A number of studies have sought to determine the abundance of Thaumarchaeotes in order to better understand numerical importance of Thaumarchaeotes to the total archaeal and prokaryotic community. Diverse methods for determining Thaumarchaeotal abundance have been employed ranging from Fluorescent In Situ Hybridization (FISH) to quantitative Polymerase Chain Reaction (qPCR) methods (DeLong et al. 1999; Wuchter et al. 2006). qPCR methods have used primers targeting the 16S rRNA of Thaumarchaeotes, the archaeal ammonia monooxygenase (amoA) gene and the acetyl-CoA carboxylase alpha subunit (accA) gene (Yakimov, La Cono and Denaro 2009; Tolar, King and Hollibaugh 2013). Thaumarchaeotal abundance in different settings often has ranged from 101 to 104 cells/ml of seawater (Yakimov, La Cono and Denaro 2009; Amano-Sato et al. 2013; Bergauer et al. 2013; Hu et al. 2013; Tolar, King and
Hollibaugh 2013). Differences in abundance have previously been observed between different depths and water masses (Amano-Sato et al. 2013) as well as between seasons (Bale et al. 2013). Direct comparison of the Thaumarchaeotal abundances between different basins is complicated through different experimental factors, such as different extraction methodologies, or different qPCR primer pairs used between studies. In this study, we characterized the abundance and highresolution phylogenetic diversity of the Thaumarchaeotal community in four marine basins (The Caspian Sea, the Great Australian Bight (GAB), the Central and Eastern Mediterranean). These four basins represent distinct oceanographic settings and a range of nutrient loadings. These basins cover enclosed (Caspian), semi-enclosed (Central and Eastern Mediterranean) and open-ocean (GAB) settings. In terms of nutrient loadings, the Caspian Sea is highly eutrophic and anthropogenically impacted (Mahmoudi et al. 2015), whereas the Central and Eastern Mediterranean are considered ultra-oligotrophic basins (Thingstad et al. 2005). The GAB would be intermediate between ¨ these basins as it is not ultra-oligotroph nor eutrophic (Kampf and Kavi 2017). Although previous studies have investigated the Thaumarchaeotal abundance in the Mediterranean (De Corte et al. 2008; Yakimov, La Cono and Denaro 2009), little is known about the Thaumarchaeotal population in the Caspian Sea and the GAB. In addition to providing insights into the microbial community in previously unexplored locations, this set of samples will also provide insights into the abundance and diversity of Thaumarchaeotes in adjacent basins with similar physical and chemical parameters (Central and Eastern Mediterranean). In this study, abundance of Thaumarchaeotes was determined using qPCR of the archaeal amoA gene. The abundance of bacteria and archaea were determined using qPCR with domainspecific 16S rRNA primers to better understand the proportion of Thaumarchaeota in relation to the total bacterial and archaeal abundance. The majority of previous studies investigating the phylogenetic diversity of Thaumarchaeota have determined diversity using binning of marker genes based on % similarity. These binning approaches are limited and lack higher resolution insights into the diversity of particular groups of microbes. We sought to investigate the phylogenetic diversity of Thaumarchaeotes, using minimum entropy decomposition (MED) (Eren et al. 2015) of the 16S rRNA sequence. MED uses Shannon entropy of highly divergent regions of a marker gene sequence alignment to distinguish between ecologically relevant groups of sequences. The combination of high-resolution community analysis and abundance comparisons will provide further insights into the diversity and abundance of this ubiquitous phylum in the oceans.
MATERIALS AND METHODS Sample collection and site characterization Water samples were collected from the following four deepsea basins: Eastern Mediterranean (Nile Deep Sea Fan), Central Mediterranean (Sirte Basin), Caspian Sea (Southern Caspian) and GAB (Bight Basin) (Fig. 1). Water was collected from five stations in the Eastern Mediterranean between 11 and 15 October 2012. Four depths were sampled at each station, representing near surface, one-third of water depth, two-thirds of water depth and near bottom. Five stations were sampled in the GAB between 7 and 14 April 2013. Four depths were sampled at each location, representing near surface, one-third of water depth, two-thirds of water depth and near bottom. Six stations
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Figure 1. Map of sampling locations. Four basins were sampled. Sampling stations are shown as white circles in the zoomed-in regions. Scale bars in the zoomed-in basin maps correspond to 100 km.
were sampled in the Caspian Sea between 27 July and 2 August 2013. Two to four depths were sampled at each location depending on water depth. Samples were collected from four stations in the Central Mediterranean between 29 January and 5 February 2014. Four depths were sampled at each station, representing near surface, one-third of water depth, two-thirds of water depth and near bottom. The majority of samples collected represent mesopelagic and bathypelagic depths. In all, 75 samples were collected as part of this study (full geochemical data are shown in Table S1, Supporting Information). In situ physical and chemical parameters were measured using a Valeport CTD equipped with sensors for temperature, salinity, dissolved oxygen and depth. Water was collected in Niskin bottles at each sampling depth and stored at –20◦ C for analysis of inorganic nutrients. Samples for microbial community analysis were collected by filtering seawater through a 142-mm, 0.2-μm nylon filter. Samples were collected in Eastern Mediterranean as described in Techtmann et al. (2015). Between 62 and 158 L per sample were filtered in the Eastern Mediterranean. Microbial samples were collected from the Caspian Sea using a McLane Pump Large Volume water sampler (McLane Labs, Falmouth MA) to filter 10.3 and 27 L of water per sample. Samples from the Central Mediterranean and the GAB were collected by recovering 20 L of water from a Niskin bottle and filtering 20 L per sample on deck. In all cases, the filters were immediately stored at –20◦ C until shipment to the lab, at which time the samples were stored at –80◦ C until processing. Differences in the methods of sample collection and volumes are due to the resources available on the cruise. For cruises where the in situ pump was available, the Large Volume water sampler was employed. When us-
ing the Large Volume water sampler, the sample collection was determined based on time. This led to varying volumes being filtered through the pump depending on the particulate load in the water leading to clogging of the filter, which resulted in decreased flow rate and overall less volume. Approximately 20 L of water was filtered for all basins other than the Eastern Mediterranean. The cell abundance in the Mediterranean is quite low due to its ultra-oligotrophic nature (Techtmann et al. 2015). This low biomass required larger volumes of water to collect similar amounts of biomass. On some cruises, the Large Volume water sampler was not available and thus samples were collected using Niskin bottles and on-board filtration was required. For cruises where Niskin bottle sampling was performed, 20 L of water was filtered as a standard volume.
Geochemical measurements Dissolved organic carbon (DOC) and inorganic nutrients were measured at the SOEST Laboratory for Analytical Biogeochemistry (University of Hawaii). DOC was measured using a Shimadzu High-Temperature TOC-L Combustion Analyzer (Shimadzu, Japan). DOC is reported as non-purgeable organic carbon (NPOC). Quality control testing for NPOC was conducted using purchased Deep Seawater Reference Material from the RSMAS Consensus Reference Materials Project (http://yyy.rsmas.miami.edu/groups/biogeochem/CRM.html). Ammonia was measured fluorometrically following the method of Kerouel and Aminot (1997). Nitrate and nitrite were analyzed via the diazo reaction based on the methods of Armstron, Stearns and Strickla (1967) and Grasshoff, Ehrhardt and Krem-
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ling (1983). Silicate measurement is based on the reduction of silicomolybdate in acidic solution to molybdenum blue by ascorbic acid (Grasshoff, Ehrhardt and Kremling 1983). Orthophosphate concentrations were determined based on the colorimetric method of Murphy and Riley (1962). For all compounds measured, concentrations were determined by the creation of a six-point standard curve made with known concentrations of the analyte of interest. The lower limit of the detection for the analytes is as follows: nitrite 0.01 μM, nitrate 0.01 μM, phosphate 0.008 μM, silicate 0.065 μM, ammonia 0.02 μM.
DNA extraction DNA was extracted from the filters using the modified Miller method as described in Techtmann et al. (2015). Quality of DNA was determined by measuring the 260/280 and 260/230 ratios using the Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA, USA). DNA concentrations were determined using the Qubit Fluorometric assay (Life Technologies, Carlsbad, CA, USA).
Preparation of standards for qPCR assays Bacterial and archaeal 16S rRNA gene concentration was determined to identify the relative contribution of archaea to the total prokaryotic community in these samples. Bacterial and archaeal 16S rRNA gene abundance was determined using qPCR according to the methods previously described in Jorgensen et al. (2012) using the following primer pairs: Bact341 and Uni519R for bacteria as well as Uni519F and Arch908R for archaea. Archaeal amoA copy number was determined using qPCR as previously described in Wuchter et al. (2006). Standards were prepared by amplifying the gene of interest from environmental DNA using the appropriate primer pair. Each amplification was performed under the following conditions: initial denaturation at 98◦ C for 2 min; 40 cycles of 98◦ C for 15 s, 30 s at the appropriate annealing temperature of the primer pair and 72◦ C for 2 min, followed by a final extension at 72◦ C for 5 min. PCR reactions included Phusion master mix (Thermo Scientific), 0.4 μM forward and reverse primers, and 1 μl of DNA template. Amplified products were run on a 1.2% agarose gel stained with SYBR Safe (Thermo Scientific. Amplicons were gel purified using the Wizard SV Gel and PCR Clean-Up System (Promega, Madison, WI). R Purified PCR products were cloned into the pCR4-TOPO TA Vector (Life Technologies) using the Topo TA cloning kit (Life Technologies). Plasmids were purified from transformants and sequenced using vector primers (M13 forward and reverse) to confirm the correct insert. Plasmids containing the proper insert were linearized by digesting the plasmid with Not1. Linearized plasmids were gel purified using the Wizard SV Gel and PCR Clean-Up System (Promega). The purified products were quantified using Qubit (Life Technologies) and used as standards for qPCR.
qPCR quantification of gene concentrations qPCR was performed on an iCycler thermocycler (Bio-Rad, Hercules, CA). Six-point standard curves were performed in triplicate with concentrations ranging from 2 × 10−4 to 20 pM. Environmental DNA was diluted 1:10 to account for potential inhibitors, and 1 μl of diluted environmental DNA was used in 20 μl qPCR reactions. The copy numbers of bacterial 16S rRNA,
archaeal 16S rRNA and archaeal amoA in environmental DNA were determined in duplicate. Total prokaryotic abundance was determined by adding the copy number of bacterial and archaeal 16S rRNA genes together. Gene copy number was corrected for the dilution factor and then normalized to the total volume of seawater that was passed through that filter to the determine the number of gene copies per ml of seawater according to the following equation: copies number per μl of DNA was multiplied by the total volume of extract (μl of DNA) and then divided by the volume of seawater filtered (ml). An analysis of vairance (ANOVA) was performed on log-transformed copy number data in order to determine if there was a significant difference in the abundance of these genes between basins. The ANOVA analysis was performed comparing samples collected from the same depth strata (epipelagic (0–200 m), mesopelagic (200–1000 m) or bathypelagic (1000–4000 m)). The Tukey honest significant difference (HSD) test was used as a post hoc test to distinguish which basins were significantly different from each other (Table S3, Supporting Information).
16S rRNA sequencing and analysis The V4 region of the 16S rRNA gene was amplified using Phusion DNA polymerase (Thermo Scientific) with universal primers 515f and barcoded 806r from all 75 samples collected as part of this project. These primers were able to amplify both bacterial and archaeal sequences. Sequencing was performed on the Illumina MiSeq according to the protocol in Caporaso et al (2012). The resulting DNA sequences were analyzed using the QIIME version 1.8.0-dev pipeline (Caporaso et al. 2010). Paired-end raw reads were assembled using fastq-join (Aronesty 2011). The assembled sequences were demultiplexed and quality filtered in QIIME to remove reads with phred scores below 20 (–q 19). Chimera detection was then performed on assembled reads using UCHIME (Edgar 2010; Edgar et al. 2011). The taxonomy for each read was assigned using Ribosmal Database Project (RDP) (Wang et al. 2007) retrained with the May 2013 Greengenes release. The total number of sequences assigned to the Thaumarchaeota was 2630 432. While the sequencing data included both bacterial and archaeal sequences, only reads classified as Thaumarchaeota were used for further analysis. MED was performed on reads classified as Thaumarchaeota (Eren et al. 2015). MED, like oligotyping, applies Shannon entropy to different nucleotide positions in a sequence alignment. MED works on the premise that not all nucleotide positions contribute equally to partitioning into ecologically meaningful bins. Positions with high entropy are indicative of ecologically meaningful differences and not due to sequencing artifacts. The total pool of nucleotides is decomposed into nodes based on the nucleotide present in the position with the highest Shannon entropy. This process is repeated iteratively until a minimum entropy threshold is achieved. This approach will distinguish between ecologically meaningful bins even for highly similar sequences, which in some cases only vary by one nucleotide. The original quality-trimming step resulted in reads with different ending positions. The data were re-trimmed to 250 bases so that all of the Thaumarchaeotal reads had the same starting and ending positions. The dataset used for MED analysis was deposited in MG-RAST Accession numbers (mgm4642815.3 - mgm4642887.3). MED was performed using the decompose command from the Oligotyping version 1.8. The minimum substantive abundance of an oligotype was set at 526 and the maximum variation allowed in each node was set at 3 nucleotides. This removed 351 057 sequences. A to-
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Table 1. Physical and chemical parameters for each basin.
Temp surface Temp deep (◦ C) Dissolved oxygen—surface (mg/L) Dissolved oxygen—deep (mg/L) Salinity (psu) Nitrate (μM) Ammonia (μM) Nitrite (μM) Inorganic P (μM) Silicate (μM) TOC (mg/L) Total nitrogen (mg/L)
E. Med
C. Med
GAB
Caspian
20.3 (17.5–26.3) 13.9 (13.7–14.5) 9.5 (9.3–11.5) 6.4 (5.9–6.9) 38.9 (38.5–39.5) 4.09 (0.01–6.95) 0.03 (
